High baud rate digital transmission systems are subject to the problem of intersymbol interference which occurs when digital data is transmitted over a narrow band channel, such as a telephone voice communication channel. Intersymbol interference arises when the digital data symbols are transmitted in such a rapid succession that the channel response to one symbol is not allowed to decay before the next successive symbol is transmitted. As a result, the demodulation of one symbol is affected (or interfered with) due to the decaying channel responses of previously transmitted symbols. Because of the economic advantages of being able to convey increasing amounts of data over a fixed available channel, the elimination of intersymbol interference, thereby permitting higher data rates, becomes of paramount importance.
Over the years, a variety of techniques for correcting for intersymbol interference have been developed. Among these is the adaptive linear transversal filter approach, such as described by R. W. Lucky in an article entitled "Automatic Equalization for Digital Communication" BSTJ, Vol. 44, pages 547-588, April 1965, and an article by J. G. Proakis and J. H. Miller entitled "An Adaptive Receiver for Digital Signalling Through Channels with Intersymbol Interference" IEEE Transactions on Information Theory, Vol. IT-15, pages 484-497, July 1969. Another significant approach to solving the problem was the development of a decision feedback equalizer (DFE), as described in an article by P. Monsen entitled "Feedback Equalization for Fading Dispersive Channels" IEEE Transaction on Information Theory, pages 56-64, January 1971 and an article by M. E. Austin entitled "Equalization of Dispersive Channels Using Decision Feedback" MIT Research Laboratories Electronics, Cambridge, Mass., Quarterly Progress Report No. 84, pages 227-243, April 1967.
More recent significant advances include the recognition that the Viterbi algorithm, which was originally developed for decoding convolutional error correcting codes, as described in an article by A. J. Viterbi, "Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm," IEEE Transactions Information Theory, IT-13, pages 260-269, April 1967, is also applicable to the demodulation of the digital data with intersymbol interference, as described in an article by G. D. Forney, Jr., "Maximum Likelihood Sequence Estimation of Digital Sequences in the Presence of Intersymbol Interference", IEEE Transactions of Information Theory, IT-18, pages 363-378, May 1972.
In addition, adaptive cancellation, as described in an article by A. Gersho and T. L. Lim entitled "Adaptive Cancellation of Intersymbol Interference for Data Transmission" BSTJ, Vol. 60, pages 1997-2021 November, 1981 has been suggested as a promising approach to this problem.
In the context of the variety of approaches for eliminating the problem of intersymbol interference, such as those suggested in the above literature, one must observe the advantages and disadvantages in applying a chosen technique to a data transmission system of interest. For example, a significant technical characteristic of the earlier equalizers, such as described in the above article by R. W. Lucky, is their linearity. Essentially this type of equalizer is a linear filter which operates to boost frequency response in the areas where the channel has a low response and to provide phase compensation. Namely, the overall tandem combination frequency response of the channel and the equalizer is rendered flat by the equalizer. While this approach performs well on channels which either add little noise or do not have significant attenuation in the pass band, on noisy channels which have significant attenuation in the pass band (actually within the Nyquist band), the linear equalizer approach-in providing the frequency response boost at attenuated frequencies-boosts or blows up the noise at those frequencies. This represents a fundamental performance limitation for linear equalizer structures when employed on noisy channels with significant band-limiting or attenuation within the Nyquist band. Adaptive tap gain adjustment algorithms for the linear transversal filter equalizer for such channels actually must be compromised by setting tap gain values which balance the noise blow-up and residual intersymbol interference phenomenon. In other words the linear equalizer will not totally eliminate intersymbol interterence if it has to amplify the noise excessively to do so.
Others of the prior art approaches mentioned above have recognized the performance limitations of linear equalizers and have resorted to non-linear processing in order to achieve improved performance. Examples of these approaches include decision feedback equalizers, The Viterbi algorithm, and adaptive cancellation as described in the Gersho et al article, referenced-above. Essential to all of these non-linear approaches is the use of surrounding symbol decisions to cancel intersymbol interference on a current demodulated pulse. The basic idea is that if the surrounding decisions are correct, then the intersymbol interference can be perfectly removed without noise enhancement, and therein lies the source of their improved performance over linear equalizers. A practical problem with this idea, however, is how to arrive at the surrounding symbol decisions. The manner in which this practical problem is handled provides a key distinction among decision feedback equalizers, adaptive cancellation and the Viterbi algorithm approaches.
More particularly, the decision feedback equalizer simply places additional linear tap gains on previous symbol decisions made by the equalizer. Thus, postcursors of the channel response can be effectively cancelled, whereas precursors cannot be cancelled, since they depend upon symbols not yet determined. As a result, the decision feedback equalizer is most effective on channels which have not significant precursors. An additional performance limitation of the decision feedback equalizer is that no provision is made for changing previously made decisions in the feedback register. This can lead to an error propagation effect, in that an erroneous decision in the feedback register can adversely affect several successive decisions.
The adaptive cancellation equalizer approach attacks the precursor cancellation limitation of the decision feedback equalizer by employing both preceding and subsequent symbol decisions made earlier by a linear equalizer. With delay provided on the raw channel input, the linear equalizer is given sufficient time to make these decisions before the data is supplied to the adaptive cancellation portion of the system.
The Viterbi algorithm processor for demodulating digital data is, perhaps, the most radical approach for providing surrounding decisions for intersymbol interference cancellation. It does so by being extremely thorough in that it does not decide what the surrounding symbols are, but simply keeps track of every possible set of surrounding symbol decisions and ultimately selects the data sequence that leads to the best match between predicted and observed channel outputs. A practical problem with this approach is the exponential increase in complexity with the channel response time duration. Namely, it is suitable only for short duration (in data pulse times) channel responses.